{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BoQAtWrTwg+zqExUVyaS3PYvzVIf3nvnQ1rbi1c+4cszFljal9sP+KeD8DGQy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\nADLeCYgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x223bfd2d0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 载入数据\n",
    "data = np.genfromtxt(\"LR-testSet2.txt\", delimiter=\",\")\n",
    "x_data = data[:,:-1]\n",
    "y_data = data[:,-1]\n",
    "\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x_data, y_data, test_size = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot(model):\n",
    "    # 获取数据值所在的范围\n",
    "    x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1\n",
    "    y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1\n",
    "\n",
    "    # 生成网格矩阵\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\n",
    "                         np.arange(y_min, y_max, 0.02))\n",
    "\n",
    "    z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据\n",
    "    z = z.reshape(xx.shape)\n",
    "    # 等高线图\n",
    "    cs = plt.contourf(xx, yy, z)\n",
    "    # 样本散点图\n",
    "    plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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caY0YmJ4Ca5IGkaLtBE+ExiqsLbTBJej04kT2SrzM9dNBjuElypi2lKLRIkNC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\nagL9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x223bfd74f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7796610169491526"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree = tree.DecisionTreeClassifier()\n",
    "dtree.fit(x_train, y_train)\n",
    "plot(dtree)\n",
    "dtree.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pFFJVVWhWzSywmcBm1sjjTWwibIvOVhNYf/lRHv/6CryEg2pYu3CjAS2rE6x5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\nGFOh/n8p5r5ItiFGWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x223c2e50a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8135593220338984"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF = RandomForestClassifier(n_estimators=50)\n",
    "RF.fit(x_train, y_train)\n",
    "plot(RF)\n",
    "RF.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
